Groundwater budget forecasting, using hybrid wavelet-ANN-GP modelling: a case study of Azarshahr Plain, East Azerbaijan, Iran

Meticulous prediction of hydrological processes, especially water budget, has an individual importance in environmental management plans. On the other hand, conservation of groundwater, a fundamental resource in arid and semi-arid areas, needs to be considered as a great priority in development plans. Prediction of a groundwater budget utilizing artificial intelligence was the scope of this study. For this aim, the Azarshahr Plain aquifer, East Azerbaijan, Iran, was selected because of its great dependence on groundwater and the necessity of cognizance of its budget in future programs. The long-term fluctuations of the water table in 13 piezometers were simulated by a wavelet-based artificial neural network (WANN) hybrid model, and their statistical gaps were covered. Then, the modelled water table was predicted for the next 12 months using genetic programming. The results of simulation and prediction were assessed by performance evaluation criteria such as R 2 , root mean squared error, mean absolute error and Nash–Sutcliffe efficiency. Thiessen polygons were then utilized, plotting the predicted unit hydrograph of the study area. The predicted water table from September 2012 to August 2013 revealed about 0.12 m depletion. Regarding the area of the Azarshahr Plain aquifer and its average storage coefficient, the aquifer budget will be reduced by about 0.3557 million cubic metres during this period.